Directing attention to onset and offset of image events for eye-head movement control
Bibliographic record
Abstract
This paper proposes a model that investigates a new avenue for attention control based on dynamic scenes. We have derived a computational model to detect abrupt changes and have examined how the most prominent change can be determined. With such a model, we explore the possibility of an attentional mechanism, in part guided by abrupt changes, for gaze control. The computational model is derived from the difference of Gaussian (DOG) model and it examines the change in the response of the DOG operator over time to determine if changes have occurred. On and off-DOG operators are used to detect "on" and "off" events respectively. The response of these operators is examined over various temporal window sizes so that changes at different rates can be found. The most salient "on" and "off" events are determined from the corresponding winner-take-all (WTA) network. The model has been tested with image sequences which have changes caused by brightness or motion and the results are satisfactory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".